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On 4th example photo the model says: >They likely share an agnostic worldview and identify as heterosexual. I wonder how the model would know that they are he
by aledevv 6mo ago
On 4th example photo the model says:
>They likely share an agnostic worldview and identify as heterosexual.
I wonder how the model would know that they are heterosexuals?
let's be careful about categorizing people so easily and in such a simplistic way.
- oliver236 6mo ago[flagged]
- deleted 6mo ago[deleted]
- micromacrofoot 6mo agothis doesn't even pass a basic logic test, why would be wounded make us seek something we want in ourselves and being whole make us seek something we aren't? there are plenty of people of any gender that have any quality you may be seeking you can't just make something up in your head and apply it to everyone
- oliver236 6mo agook
- augment_me 6mo agoA bit like "they do not have cancer", if you are fitting to a distribution you will have the best results by estimating an average. Being hetero is the majority/average, so a good prediction. But doing this on a 20-way parlay like in this case will almost always fail.
- piculet 6mo agoTagged both me (male) and my male partner as heterosexuals. I think there is still some learning to do on that front. Rainbow merchandise has not been as widely adopted as you might think.
- wongarsu 6mo agoMany homosexuals are visually identifiable as such (with reasonable certainty), some by accident and some by deliberate signalling. I can easily see how the absence of any such signals could end up as a classification as heterosexual, even though it really should put them in the "unknown" category. Of course any automated classification of that kind quickly gets problematic in multiple ways. In the EU it's a fast-track to getting your AI labeled as a "high risk AI system" that has higher requirements for quality control, ensuring fairness and user choice, etc
- xigoi 6mo ago> I wonder how the model would know that they are heterosexuals? It’s about 95% likely to be correct, which is very effective at scaring statistically illiterate people.